Ignite Creation Date:
2025-12-25 @ 3:32 AM
Ignite Modification Date:
2026-01-09 @ 11:31 PM
Study NCT ID:
NCT06540105
Status:
RECRUITING
Last Update Posted:
2024-09-20
First Post:
2024-07-29
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Leveraging Artificial Intelligence and Multi-Omics Data to Predict Opioid Addiction
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D009293', 'term': 'Opioid-Related Disorders'}], 'ancestors': [{'id': 'D000079524', 'term': 'Narcotic-Related Disorders'}, {'id': 'D019966', 'term': 'Substance-Related Disorders'}, {'id': 'D064419', 'term': 'Chemically-Induced Disorders'}, {'id': 'D001523', 'term': 'Mental Disorders'}]}}, 'protocolSection': {'designModule': {'bioSpec': {'retention': 'SAMPLES_WITH_DNA', 'description': 'saliva for DNA sequencing, stool sample for microbiome analyses and blood/plasma for metabolomic analyses'}, 'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'CASE_ONLY'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 300}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2024-08-26', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-08', 'completionDateStruct': {'date': '2027-09-30', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-09-18', 'studyFirstSubmitDate': '2024-07-29', 'studyFirstSubmitQcDate': '2024-08-01', 'lastUpdatePostDateStruct': {'date': '2024-09-20', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-08-06', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2027-06-29', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'opioid use disorder at baseline based on the DSM-5 criteria', 'timeFrame': 'baseline', 'description': 'risk of opioid use disorder based on genomics, social determinants of health, microbiome, and other clinical data. Patients with OUD will be enrolled and compared to healthy controls using data from external datasets such as NIH All of Us Research Program. Diagnosis of opioid use disorder will be based on the DSM-5 criteria for opioid use disorder'}], 'secondaryOutcomes': [{'measure': 'response to opioid use disorder treatment after 6 months from baseline based on the DSM-5 criteria', 'timeFrame': '6 months', 'description': 'prediction of response to opioid use disorder treatment. Remission from opioid use disorder will be based on DSM-5 criteria for remission from substance use disorder'}, {'measure': 'opioid use disorder remission after 6 months from baseline based on the DSM-5 criteria for remission', 'timeFrame': '6 months', 'description': 'prediction of remission of opioid use disorder based on the DSM-5 criteria for remission from substance use disorder'}, {'measure': 'severity of opioid use disorder at baseline based on the DSM-5 criteria for opioid use disorder', 'timeFrame': 'baseline', 'description': 'prediction of severity of opioid use disorder based on the DSM-5 criteria for opioid use disorder'}, {'measure': 'co-substance use at baseline', 'timeFrame': 'baseline', 'description': 'risk of developing co-substance use disorder based on DSM-5 criteria for substance use disorders'}, {'measure': 'Relapse to opioid use disorder after 6 months from baseline', 'timeFrame': '6 months', 'description': 'risk of relapse to opioid use disorder based on DSM-5 criteria for opioid use disorder'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['genomics', 'artificial intelligence', 'microbiome', 'opioid use disorder', 'opioid addiction'], 'conditions': ['Opioid Use Disorder', 'Addiction, Opioid']}, 'descriptionModule': {'briefSummary': "The primary goal of this proposal is to validate a novel genomic and microbiome predictive model that may be used to assess a person's risk of developing opioid use disorder (OUD). The following will be tested: (1) MODUS (Measuring risk for Opioid use Disorder Using SNPs), which is a genomic panel consisting of a set number of proven single nucleotide polymorphisms (SNP) that utilizes machine learning to determine an individual's risk; and (2) MICROUD (MICRObiome for Opioid Use Disorder), which will be a novel microbiome prediction panel for OUD risk. MODUS and MICROUD will be developed using existing public datasets with genomic and microbiome data (e.g., All of Us, Human Microbiome Project). During development of these predictive models, in parallel, an external prospective validation cohort will be recruited consisting of subjects from the University of California, San Diego, Veteran Affairs of San Diego, and Veteran Affairs of Palo Alto (each site with separate IRB). The hypothesis is that MODUS and MICROUD will have high predictive potential for identifying high risk patients for OUD.", 'detailedDescription': "The primary goal of this proposal is to validate a novel genomic and microbiome predictive model that may be used to assess a person's risk of developing opioid use disorder (OUD). The following will be tested: (1) MODUS (Measuring risk for Opioid use Disorder Using SNPs), which is a genomic panel consisting of a set number of proven single nucleotide polymorphisms (SNP) that utilizes machine learning to determine an individual's risk; and (2) MICROUD (MICRObiome for Opioid Use Disorder), which will be a novel microbiome prediction panel for OUD risk. MODUS and MICROUD will be developed using existing public datasets with genomic and microbiome data (e.g., All of Us, Human Microbiome Project). During development of these predictive models, in parallel, an external prospective validation cohort will be recruited consisting of subjects from the University of California, San Diego, Veteran Affairs of San Diego, and Veteran Affairs of Palo Alto (each site with separate IRB). The hypothesis is that MODUS and MICROUD will have high predictive potential for identifying high risk patients for OUD.\n\nSpecific Aim 1. Validate a novel genomic predictive panel assay - termed MODUS - in a prospective observational study that aims to recruit 300 subjects (\\~200 from UCSD and VA San Diego) with a history of OUD. This genomic panel will be developed separately but then validated on the study population. Healthy control data will be used from a publicly-available de-identified genomic dataset (All of Us Research Program) .\n\nSpecific Aim 2. Validate a novel microbiome predictive panel assay - termed MICROUD - in a prospective observational study that aims to recruit 300 subjects (\\~200 from UCSD and VA San Diego) with a history of OUD. This microbiome panel will be developed separately but then validated on the study population. Healthy control data will be used from a publicly-available de-identified microbiome dataset (Human Microbiome Project)."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'For the prospective observational study described , adult subjects (≥18 years old) will be recruited with active or previous history of opioid addiction from various clinical settings, including chronic pain clinics, emergency department, operating room, addiction clinics, family medicine/primary care clinics, and surgical clinics', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* diagnosis of OUD (active or in remission) defined by the DSM-5 criteria\n* age ≥ 18 years old\n\nExclusion Criteria:\n\n* inability to participate independently with the study (i.e. dementia)\n* chronic opioid use that is not consistent with a diagnosis of OUD\n* patients that are pregnant\n* children\n* institutionalized individuals\n* non-English speaking subjects as there are several surveys without appropriate translation and with sensitive information (e.g., questions about mental health and history of drug use) that is required to complete the study.'}, 'identificationModule': {'nctId': 'NCT06540105', 'briefTitle': 'Leveraging Artificial Intelligence and Multi-Omics Data to Predict Opioid Addiction', 'organization': {'class': 'OTHER', 'fullName': 'University of California, San Diego'}, 'officialTitle': 'Leveraging Artificial Intelligence and Multi-Omics Data to Predict Opioid Addiction', 'orgStudyIdInfo': {'id': '810491'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Opioid Use Disorder', 'description': 'Subjects with a history of opioid use disorder'}]}, 'contactsLocationsModule': {'locations': [{'zip': '92037', 'city': 'La Jolla', 'state': 'California', 'status': 'RECRUITING', 'country': 'United States', 'contacts': [{'name': 'Rodney Gabriel, MD, MAS', 'role': 'CONTACT', 'email': 'ragabriel@health.ucsd.edu', 'phone': '8586637747'}, {'name': 'Brandon Palugod, BS', 'role': 'CONTACT', 'email': 'bmpalugod@health.ucsd.edu'}, {'name': 'Rodney A Gabriel, MD, MAS', 'role': 'CONTACT'}], 'facility': 'University of California, San Diego', 'geoPoint': {'lat': 32.84727, 'lon': -117.2742}}], 'centralContacts': [{'name': 'Rodney A Gabriel, MD', 'role': 'CONTACT', 'email': 'ragabriel@health.ucsd.edu', 'phone': '8586637747'}, {'name': 'Sesh Mudumbai, MD', 'role': 'CONTACT', 'email': 'mudumbai@stanford.edu'}], 'overallOfficials': [{'name': 'Rodney A Gabriel, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'University of California, San Diego'}]}, 'ipdSharingStatementModule': {'infoTypes': ['STUDY_PROTOCOL', 'SAP', 'ICF', 'CSR', 'ANALYTIC_CODE'], 'timeFrame': 'after conclusion of the study for indeterminate amount of time', 'ipdSharing': 'YES', 'description': 'A publicly available biobank with be developed with de-identified data that may be shared with external researchers after approval.', 'accessCriteria': 'appropriate data use agreement and IRB approval'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University of California, San Diego', 'class': 'OTHER'}, 'collaborators': [{'name': 'VA Palo Alto Health Care System', 'class': 'FED'}, {'name': 'San Diego Veterans Healthcare System', 'class': 'FED'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Associate Professor', 'investigatorFullName': 'Rodney Gabriel', 'investigatorAffiliation': 'University of California, San Diego'}}}}